
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput


class 			SCREAMING_SNAKE_CASE__      (     UpperCAmelCase__):







				def __init__(       self						,				A_						,				A_						,				A_     )->		Any:







											'''simple docstring'''






											UpperCamelCase   			=     dataset
											UpperCamelCase   			=     process
											UpperCamelCase   			=     params







				def __len__(       self     )->		int:







											'''simple docstring'''






											return len(self.dataset     )







				def __getitem__(       self						,				A_     )->		int:







											'''simple docstring'''






											UpperCamelCase   			=     self.dataset[i]
											UpperCamelCase   			=     self.process(_a						,				**self.params     )
											return processed


class 			SCREAMING_SNAKE_CASE__      (     UpperCAmelCase__):







				def __init__(       self						,				A_						,				A_						,				A_						,				A_=None     )->		str:







											'''simple docstring'''






											UpperCamelCase   			=     loader
											UpperCamelCase   			=     infer
											UpperCamelCase   			=     params
											if loader_batch_size == 1:
																		# Let's spare some time by deactivating altogether
																		UpperCamelCase   			=     None
											UpperCamelCase   			=     loader_batch_size

											# Internal bookkeeping
											UpperCamelCase   			=     None
											UpperCamelCase   			=     None







				def __len__(       self     )->		Any:







											'''simple docstring'''






											return len(self.loader     )







				def __iter__(       self     )->		str:







											'''simple docstring'''






											UpperCamelCase   			=     iter(self.loader     )
											return self







				def 						UpperCAmelCase_    (       self     )->		str:







											'''simple docstring'''






											if isinstance(self._loader_batch_data						,				torch.Tensor     ):
																		# Batch data is simple tensor, just fetch the slice
																		UpperCamelCase   			=     self._loader_batch_data[self._loader_batch_index]
											else:
																		# Batch data is assumed to be BaseModelOutput (or dict)
																		UpperCamelCase   			=     {}
																		for k, element in self._loader_batch_data.items():
																									if isinstance(_a						,				_a     ):
																																# Convert ModelOutput to tuple first
																																UpperCamelCase   			=     element.to_tuple()
																																if isinstance(element[0]						,				torch.Tensor     ):
																																							UpperCamelCase   			=     tuple(el[self._loader_batch_index].unsqueeze(0     ) for el in element     )
																																elif isinstance(element[0]						,				np.ndarray     ):
																																							UpperCamelCase   			=     tuple(np.expand_dims(el[self._loader_batch_index]						,				0     ) for el in element     )
																																continue
																									if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(_a						,				_a     ):
																																# Those are stored as lists of tensors so need specific unbatching.
																																if isinstance(element[0]						,				torch.Tensor     ):
																																							UpperCamelCase   			=     tuple(el[self._loader_batch_index].unsqueeze(0     ) for el in element     )
																																elif isinstance(element[0]						,				np.ndarray     ):
																																							UpperCamelCase   			=     tuple(np.expand_dims(el[self._loader_batch_index]						,				0     ) for el in element     )
																																continue
																									if element is None:
																																# This can happen for optional data that get passed around
																																UpperCamelCase   			=     None
																									elif isinstance(element[self._loader_batch_index]						,				torch.Tensor     ):
																																# Take correct batch data, but make it looked like batch_size=1
																																# For compatibility with other methods within transformers

																																UpperCamelCase   			=     element[self._loader_batch_index].unsqueeze(0     )
																									elif isinstance(element[self._loader_batch_index]						,				np.ndarray     ):
																																# Take correct batch data, but make it looked like batch_size=1
																																# For compatibility with other methods within transformers
																																UpperCamelCase   			=     np.expand_dims(element[self._loader_batch_index]						,				0     )
																									else:
																																# This is typically a list, so no need to `unsqueeze`.
																																UpperCamelCase   			=     element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
																		UpperCamelCase   			=     self._loader_batch_data.__class__(_a     )
											self._loader_batch_index += 1
											return result







				def 						UpperCAmelCase_    (       self     )->		int:







											'''simple docstring'''






											if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
																		# We are currently unrolling a batch so we just need to return
																		# the current item within a batch
																		return self.loader_batch_item()

											# We're out of items within a batch
											UpperCamelCase   			=     next(self.iterator     )
											UpperCamelCase   			=     self.infer(_a						,				**self.params     )
											# We now have a batch of "inferred things".
											if self.loader_batch_size is not None:
																		# Try to infer the size of the batch
																		if isinstance(_a						,				torch.Tensor     ):
																									UpperCamelCase   			=     processed
																		else:
																									UpperCamelCase   			=     list(processed.keys()     )[0]
																									UpperCamelCase   			=     processed[key]
																		if isinstance(_a						,				_a     ):
																									UpperCamelCase   			=     len(_a     )
																		else:
																									UpperCamelCase   			=     first_tensor.shape[0]
																		if 0 < observed_batch_size < self.loader_batch_size:
																									# could be last batch so we can't unroll as many
																									# elements.
																									UpperCamelCase   			=     observed_batch_size
																		# Setting internal index to unwrap the batch
																		UpperCamelCase   			=     processed
																		UpperCamelCase   			=     0
																		return self.loader_batch_item()
											else:
																		# We're not unrolling batches
																		return processed


class 			SCREAMING_SNAKE_CASE__      (     UpperCAmelCase__):







				def __init__(       self						,				A_						,				A_						,				A_						,				A_=None     )->		str:







											'''simple docstring'''






											super().__init__(_a						,				_a						,				_a     )







				def __iter__(       self     )->		Tuple:







											'''simple docstring'''






											UpperCamelCase   			=     iter(self.loader     )
											UpperCamelCase   			=     None
											return self







				def 						UpperCAmelCase_    (       self     )->		Tuple:







											'''simple docstring'''






											if self.subiterator is None:
																		UpperCamelCase   			=     self.infer(next(self.iterator     )						,				**self.params     )
											try:
																		# Try to return next item
																		UpperCamelCase   			=     next(self.subiterator     )
											except StopIteration:
																		# When a preprocess iterator ends, we can start lookig at the next item
																		# ChunkIterator will keep feeding until ALL elements of iterator
																		# all have created their subiterator and have been iterating against.
																		#
																		# Another way to look at it, is we're basically flattening lists of lists
																		# into a single list, but with generators
																		UpperCamelCase   			=     self.infer(next(self.iterator     )						,				**self.params     )
																		UpperCamelCase   			=     next(self.subiterator     )
											return processed


class 			SCREAMING_SNAKE_CASE__      (     UpperCAmelCase__):







				def __iter__(       self     )->		List[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     iter(self.loader     )
											return self







				def 						UpperCAmelCase_    (       self     )->		Union[str, Any]:







											'''simple docstring'''






											UpperCamelCase   			=     False
											UpperCamelCase   			=     []
											if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
																		while self._loader_batch_index < self.loader_batch_size:
																									UpperCamelCase   			=     self.loader_batch_item()
																									UpperCamelCase   			=     item.pop('is_last'     )
																									accumulator.append(_a     )
																									if is_last:
																																return accumulator

											while not is_last:
																		UpperCamelCase   			=     self.infer(next(self.iterator     )						,				**self.params     )
																		if self.loader_batch_size is not None:
																									if isinstance(_a						,				torch.Tensor     ):
																																UpperCamelCase   			=     processed
																									else:
																																UpperCamelCase   			=     list(processed.keys()     )[0]
																																UpperCamelCase   			=     processed[key]
																									if isinstance(_a						,				_a     ):
																																UpperCamelCase   			=     len(_a     )
																									else:
																																UpperCamelCase   			=     first_tensor.shape[0]
																									if 0 < observed_batch_size < self.loader_batch_size:
																																# could be last batch so we can't unroll as many
																																# elements.
																																UpperCamelCase   			=     observed_batch_size
																									UpperCamelCase   			=     processed
																									UpperCamelCase   			=     0
																									while self._loader_batch_index < self.loader_batch_size:
																																UpperCamelCase   			=     self.loader_batch_item()
																																UpperCamelCase   			=     item.pop('is_last'     )
																																accumulator.append(_a     )
																																if is_last:
																																							return accumulator
																		else:
																									UpperCamelCase   			=     processed
																									UpperCamelCase   			=     item.pop('is_last'     )
																									accumulator.append(_a     )
											return accumulator


class 			SCREAMING_SNAKE_CASE__      (     UpperCAmelCase__):







				def __init__(       self						,				A_						,				A_     )->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     dataset
											UpperCamelCase   			=     key







				def __len__(       self     )->		Tuple:







											'''simple docstring'''






											return len(self.dataset     )







				def __getitem__(       self						,				A_     )->		Any:







											'''simple docstring'''






											return self.dataset[i][self.key]


class 			SCREAMING_SNAKE_CASE__      (     UpperCAmelCase__):







				def __init__(       self						,				A_						,				A_						,				A_     )->		str:







											'''simple docstring'''






											UpperCamelCase   			=     dataset
											UpperCamelCase   			=     keya
											UpperCamelCase   			=     keya







				def __len__(       self     )->		Any:







											'''simple docstring'''






											return len(self.dataset     )







				def __getitem__(       self						,				A_     )->		int:







											'''simple docstring'''






											return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}










"""simple docstring"""




from __future__ import annotations

from decimal import Decimal

from numpy import array



def        a__					(							snake_case__							)		->   list[list[float]]:
			lowerCamelCase      =   Decimal

			# Check if the provided matrix has 2 rows and 2 columns
			# since this implementation only works for 2x2 matrices
			if len(snake_case__							) == 2 and len(matrix[0]							) == 2 and len(matrix[1]							) == 2:
						# Calculate the determinant of the matrix
						lowerCamelCase      =   float(
						    d(matrix[0][0]							) * d(matrix[1][1]							) - d(matrix[1][0]							) * d(matrix[0][1]							)							)
						if determinant == 0:
									raise ValueError("""This matrix has no inverse."""							)

						# Creates a copy of the matrix with swapped positions of the elements
						lowerCamelCase      =   [[0.0, 0.0], [0.0, 0.0]]
						lowerCamelCase ,  lowerCamelCase      =   matrix[1][1], matrix[0][0]
						lowerCamelCase ,  lowerCamelCase      =   -matrix[1][0], -matrix[0][1]

						# Calculate the inverse of the matrix
						return [
						    [(float(d(snake_case__							)							) / determinant) or 0.0 for n in row] for row in swapped_matrix
						]
			elif (
			    len(snake_case__							) == 3
			    and len(matrix[0]							) == 3
			    and len(matrix[1]							) == 3
			    and len(matrix[2]							) == 3
			):
						# Calculate the determinant of the matrix using Sarrus rule
						lowerCamelCase      =   float(
						    (
						        (d(matrix[0][0]							) * d(matrix[1][1]							) * d(matrix[2][2]							))
						        + (d(matrix[0][1]							) * d(matrix[1][2]							) * d(matrix[2][0]							))
						        + (d(matrix[0][2]							) * d(matrix[1][0]							) * d(matrix[2][1]							))
						    )
						    - (
						        (d(matrix[0][2]							) * d(matrix[1][1]							) * d(matrix[2][0]							))
						        + (d(matrix[0][1]							) * d(matrix[1][0]							) * d(matrix[2][2]							))
						        + (d(matrix[0][0]							) * d(matrix[1][2]							) * d(matrix[2][1]							))
						    )							)
						if determinant == 0:
									raise ValueError("""This matrix has no inverse."""							)

						# Creating cofactor matrix
						lowerCamelCase      =   [
						    [d(0.0							), d(0.0							), d(0.0							)],
						    [d(0.0							), d(0.0							), d(0.0							)],
						    [d(0.0							), d(0.0							), d(0.0							)],
						]
						lowerCamelCase      =   (d(matrix[1][1]							) * d(matrix[2][2]							)) - (
						    d(matrix[1][2]							) * d(matrix[2][1]							)
						)
						lowerCamelCase      =   -(
						    (d(matrix[1][0]							) * d(matrix[2][2]							)) - (d(matrix[1][2]							) * d(matrix[2][0]							))
						)
						lowerCamelCase      =   (d(matrix[1][0]							) * d(matrix[2][1]							)) - (
						    d(matrix[1][1]							) * d(matrix[2][0]							)
						)
						lowerCamelCase      =   -(
						    (d(matrix[0][1]							) * d(matrix[2][2]							)) - (d(matrix[0][2]							) * d(matrix[2][1]							))
						)
						lowerCamelCase      =   (d(matrix[0][0]							) * d(matrix[2][2]							)) - (
						    d(matrix[0][2]							) * d(matrix[2][0]							)
						)
						lowerCamelCase      =   -(
						    (d(matrix[0][0]							) * d(matrix[2][1]							)) - (d(matrix[0][1]							) * d(matrix[2][0]							))
						)
						lowerCamelCase      =   (d(matrix[0][1]							) * d(matrix[1][2]							)) - (
						    d(matrix[0][2]							) * d(matrix[1][1]							)
						)
						lowerCamelCase      =   -(
						    (d(matrix[0][0]							) * d(matrix[1][2]							)) - (d(matrix[0][2]							) * d(matrix[1][0]							))
						)
						lowerCamelCase      =   (d(matrix[0][0]							) * d(matrix[1][1]							)) - (
						    d(matrix[0][1]							) * d(matrix[1][0]							)
						)

						# Transpose the cofactor matrix (Adjoint matrix)
						lowerCamelCase      =   array(snake_case__							)
						for i in range(3							):
									for j in range(3							):
												lowerCamelCase      =   cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
						lowerCamelCase      =   array(snake_case__							)
						for i in range(3							):
									for j in range(3							):
												inverse_matrix[i][j] /= d(snake_case__							)

        # Calculate the inverse of the matrix
						return [[float(d(snake_case__							)							) or 0.0 for n in row] for row in inverse_matrix]
			raise ValueError("""Please provide a matrix of size 2x2 or 3x3."""							)



